Systems and methods for image correction

ABSTRACT

The present disclosure provides a system and method for motion field generation and image correction. The method may include obtaining a plurality of first sets of magnetic resonance (MR) image data of an object generated based on a plurality of first sets of imaging sequences. The method may include obtaining a motion curve of the object. The method may include obtaining position emission tomography (PET) image data of the object generated in a scanning time period. The method may include generating one or more target motion fields corresponding to the scanning time period based on the plurality of first sets of MR image data and the motion curve. The method may include generating one or more corrected PET images by correcting, based on the one or more target motion fields, the PET image data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Patent Application No.201910810494.2, filed on Aug. 29, 2019, the contents of which are herebyincorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to image technology, and moreparticularly to systems and methods for motion field generation andimage correction.

BACKGROUND

With the development of imaging technology, various techniques arecombined for disease diagnoses. For example, a multi-modality system(e.g., a positron emission tomography (PET) system combined with amagnetic resonance imaging (MRI) system) can be used to exam softtissue. However, the image quality of images (e.g., images of chestand/or abdomen) generated by the PET-MRI system is generally affected bya motion (e.g., respiratory motion, heartbeat, etc.) of a scannedobject. Exemplary motion artifact correction techniques may include areconstruction technique guided by a motion waveform generated from arespiratory belt or a navigator, a correction technique based on amotion field that is generated based on special MRI sequence(s), or thelike. However, the reconstruction technique guided by the motionwaveform can only reconstruct a corrected image corresponding to aspecific respiratory phase or state, without using image data of otherrespiratory phases or states, thereby resulting in a relatively lowsignal-to-noise ratio (SNR) of the corrected image. The correctiontechnique based on special MRI sequence(s) may make full use of theimage data generated in different respiratory phases or states, andachieve motion artifact correction between different respiratory phasesor states, thereby ensuring the image quality. However, the correctiontechnique based on special MRI sequence(s) relies on the special MRIsequence(s), and can only perform motion artifact correction for imagedata acquired during a period in which the special MRI sequence(s) areused. Accordingly, other image data acquired during other periods inwhich the special MRI sequence(s) are not used may not be efficientlycorrected. If the motion field generated based on the special MRIsequence(s) is used to correct the other image data acquired during theother periods, the other image data may not be efficiently corrected asthe motion of the scanned object may be unstable. Therefore, it isdesirable to provide systems and methods for generating motion field(s)and correcting images efficiently.

SUMMARY

In one aspect of the present disclosure, a method for motion fieldgeneration and image correction is provided. The method may include oneor more operations. The one or more operations may be implemented on acomputing device having one or more processors and one or more storagedevices. The one or more processors may obtain a plurality of first setsof magnetic resonance (MR) image data of an object generated based on aplurality of first sets of imaging sequences. The plurality of firstsets of imaging sequences may be separated in a scanning time period.The one or more processors may obtain a motion curve of the object. Themotion curve may be associated with a physiological motion of the objectin the scanning time period. The one or more processors may obtainposition emission tomography (PET) image data of the object generated inthe scanning time period. The one or more processors may generate one ormore target motion fields corresponding to the scanning time periodbased on the plurality of first sets of MR image data and the motioncurve. The one or more processors may generate one or more corrected PETimages by correcting, based on the one or more target motion fields, thePET image data.

In some embodiments, the method may further include obtaining aplurality of second sets of MR image data of the object generated basedon a plurality of second sets of imaging sequences. The plurality ofsecond sets of imaging sequences may be interleaved with the pluralityof first sets of imaging sequences.

In some embodiments, the method may include generating one or morecorrected MR images by correcting, based on the one or more targetmotion fields, the plurality of second sets of MR image data.

In some embodiments, the plurality of first sets of imaging sequencesmay be sparsely interspersed between the plurality of second sets ofimaging sequences.

In some embodiments, the motion curve may include at least one of arespiratory motion curve or a cardiac motion curve.

In some embodiments, a generation of at least a portion of the PET imagedata may be simultaneous to a generation of the plurality of first setsof MR image data.

In some embodiments, the PET image data may include PET raw data or datacorresponding to one or more PET images reconstructed based on the PETraw data.

In some embodiments, for generating one or more target motion fieldscorresponding to the scanning time period, the method may includeobtaining a plurality of first sets of motion fields by generating,based on the motion curve, at least one first set of motion fieldscorresponding to each first set of time intervals in which one first setof MR image data among the plurality of first sets of MR image data aregenerated. In some embodiments, the method may include generating theone or more target motion fields based on the plurality of first sets ofmotion fields.

In some embodiments, the motion curve may include a respiratory motioncurve. In some embodiments, for generating, based on the motion curve,at least one first set of motion fields corresponding to each first setof time intervals in which one first set of MR image data among theplurality of first sets of MR image data is generated, the method mayinclude determining a plurality of respiratory phases of a respiratorymotion of the object. The method may include determining a plurality ofpieces of MR image data corresponding to the plurality of respiratoryphases by determining, based on the plurality of first sets of MR imagedata and the respiratory motion curve, a piece of MR image datacorresponding to each of the plurality of respiratory phases. The methodmay include reconstructing a plurality of images corresponding to theplurality of respiratory phases based on the plurality of pieces of MRimage data. The method may include generating the at least one first setof motion fields based on the plurality of images corresponding to theplurality of respiratory phases.

In some embodiments, for determining a plurality of respiratory phasesof a respiratory motion of the object, the method may include mayinclude determining a plurality of first sets of time intervals in whichthe plurality of first sets of MR image data are generated. The methodmay include determining at least one portion of the respiratory motioncurve corresponding to the plurality of first sets of time intervals.The method may include determining the plurality of respiratory phasesof the respiratory motion of the object based on the at least oneportion of the respiratory motion curve.

In some embodiments, for determining a plurality of pieces of MR imagedata corresponding to the plurality of respiratory phases, the methodmay include dividing the plurality of first sets of MR image data intothe plurality of pieces of MR image data corresponding to the pluralityof respiratory phases, based on the plurality of first sets of MR imagedata and the plurality of respiratory phases.

In some embodiments, for generating the one or more target motion fieldsbased on the plurality of first sets of motion fields, the method mayinclude determining, based on the scanning time period and a pluralityof first sets of time intervals in which the plurality of first sets ofMR image data are generated, a plurality of second sets of timeintervals. The method may include obtaining a plurality of second setsof motion fields by generating, based on the plurality of first sets ofmotion fields, at least one second set of motion fields corresponding toeach second set of time intervals of the plurality of second sets oftime intervals. The method may include generating the one or more targetmotion fields based on the plurality of first sets of motion fields andthe plurality of second sets of motion fields.

In some embodiments, for generating, based on the plurality of firstsets of motion fields, at least one second set of motion fieldscorresponding to each second set of time intervals of the plurality ofsecond sets of time intervals, the method may include designating one ormore first sets of motion fields corresponding to one of the pluralityof first sets of time intervals that is adjacent to the each second setof time intervals as the at least one second set of motion fields.

In some embodiments, for generating, based on the plurality of firstsets of motion fields, at least one second set of motion fieldscorresponding to each second set of time intervals of the plurality ofsecond sets of time intervals, the method may include generating the atleast one second set of motion fields corresponding to the each secondset of time intervals by fitting one or more first sets of motion fieldscorresponding to two of the plurality of first sets of time intervalsthat are adjacent to the each second set of time intervals.

In some embodiments, for generating the one or more target motion fieldsbased on the plurality of first sets of motion fields and the pluralityof second sets of motion fields, the method may include designating theplurality of first sets of motion fields and the plurality of secondsets of motion fields as the one or more target motion fields.

In some embodiments, for generating the one or more target motion fieldsbased on the plurality of first sets of motion fields, the method mayinclude generating the one or more target motion fields by fitting theplurality of first sets of motion fields.

In some embodiments, the each first set of time intervals may includeone or more respiratory cycles of the object.

In some embodiments, the plurality of first sets of imaging sequencesmay include at least one of a multi-cycle radial imaging sequence, aspiral imaging sequence, a random imaging sequence, or a radial imagingsequence with a golden-angle scheme.

In another aspect of the present disclosure, a system for motion fieldgeneration and image correction is provided. The system may include atleast one storage device storing a set of instructions, and at least oneprocessor in communication with the storage device. When executing theset of instructions, the at least one processor may be configured tocause the system to perform operations. The operation may includeobtaining a plurality of first sets of magnetic resonance (MR) imagedata of an object generated based on a plurality of first sets ofimaging sequences. The plurality of first sets of imaging sequences maybe separated in a scanning time period. The operation may includeobtaining a motion curve of the object. The motion curve may beassociated with a physiological motion of the object in the scanningtime period. The operation may include obtaining position emissiontomography (PET) image data of the object generated in the scanning timeperiod. The operation may include generating one or more target motionfields corresponding to the scanning time period based on the pluralityof first sets of MR image data and the motion curve. The operation mayinclude generating one or more corrected PET images by correcting, basedon the one or more target motion fields, the PET image data.

In still another aspect of the present disclosure, a non-transitorycomputer-readable medium storing at least one set of instructions isprovided. When executed by at least one processor, the at least one setof instructions may direct the at least one processor to perform amethod. The method may include obtaining a plurality of first sets ofmagnetic resonance (MR) image data of an object generated based on aplurality of first sets of imaging sequences. The plurality of firstsets of imaging sequences may be separated in a scanning time period.The method may include obtaining a motion curve of the object. Themotion curve may be associated with a physiological motion of the objectin the scanning time period. The method may include obtaining positionemission tomography (PET) image data of the object generated in thescanning time period. The method may include generating one or moretarget motion fields corresponding to the scanning time period based onthe plurality of first sets of MR image data and the motion curve. Themethod may include generating one or more corrected PET images bycorrecting, based on the one or more target motion fields, the PET imagedata.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary image processingsystem according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing device may be implemented according to some embodiments ofthe present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device on which the terminalmay be implemented according to some embodiments of the presentdisclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for imageprocessing according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generatingtarget motion fields according to some embodiments of the presentdisclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determininga plurality of respiratory phases of a respiratory motion of an objectaccording to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for generatingtarget motion fields based on a plurality of first sets of motion fieldsaccording to some embodiments of the present disclosure; and

FIG. 9 is a schematic diagram illustrating an exemplary process forgenerating target motion fields and correcting image data based on thetarget motion fields according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by anotherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processor 210 as illustrated in FIG. 2) may beprovided on a computer-readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedin connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

As used herein, a representation of an object (e.g., a patient, or aportion thereof) in an image may be referred to the object for brevity.For instance, a representation of an organ or tissue (e.g., the heart,the liver, a lung, etc., of a patient) in an image may be referred to asthe organ or tissue for brevity. As used herein, an operation on arepresentation of an object in an image may be referred to as anoperation on the object for brevity. For instance, a segmentation of aportion of an image including a representation of an organ or tissue(e.g., the heart, the liver, a lung, etc., of a patient) from the imagemay be referred to as a segmentation of the organ or tissue for brevity.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

A positron emission computed tomography (PET) device or system may exammetabolic activity of an object based on an aggregation of a substance(injected into the object) in metabolism. The PET technology may be arelatively efficient imaging technology for clinical diagnoses in thefield of nuclear medicine. Using the PET technology, one or moreradioactive tracer isotopes (also referred to as radionuclides) (e.g.,18F, 11C, etc.) may be injected into the object to label one or moresubstances (e.g., glucose, proteins, nucleic acids, fatty acids, etc.)of the object. The radionuclides may release positrons in a process ofdecay. A positron may move a certain distance (e.g., from tenths of amillimeter to one or more millimeters) and/or annihilate whenencountering an electron. Accordingly, a pair of photons having anenergy of 511 KeV and opposite directions may be generated. The pair ofphotons may be captured by a highly sensitive detector to generate imagedata, and then scatter correction and/or random correction may beperformed on the image data. A plurality of positrons may be collectedand/or processed similarly, and three-dimensional (3D) images of theobject may be obtained, thereby facilitating diagnoses of the object.

In magnetic resonance imaging (MRI), the object may be placed in amagnetic field, and a radio frequency (RF) pulse may be used to excitehydrogen nuclei in the object, thereby causing resonance of the hydrogennuclei, and absorbing energy. After the RF pulse is removed, thehydrogen nuclei may emit signals at a specific frequency, and releasethe absorbed energy. The released energy may be recorded by a receiverin vitro, and an image may be generated. The image may be referred to asa nuclear magnetic resonance image. The magnetic resonance imaging (MRI)device or system may introduce no damage of ionizing radiation to theobject, and may have many outstanding characteristics (e.g., multipleparameters, large amount of information, multi-directional imaging, highresolution of soft tissue, etc.). The MRI technology may be widely usedin clinical diagnoses of diseases, and become an indispensable detectiontechnique for some specific lesions.

A combination of the PET technology and the MRI technology may achievesynchronous data acquisition and image fusion, obtain more accurateinformation about the structure(s), function(s), and metabolism of thescanned object, and reduce or eliminate radiation received by thescanned object. Therefore, the combination of PET and MRI may be of agreat value for improving the efficiency and/or accuracy of thediagnoses and treatments of diseases.

The present disclosure relates to systems and methods for imagecorrection. It should be noted that the descriptions of image correctionfor PET image(s) and/or MRI image(s) in the present disclosure aremerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. In some embodiments, aplurality of first sets of magnetic resonance (MR) image data of anobject generated based on a plurality of first sets of imaging sequencesmay be obtained. The plurality of first sets of imaging sequences may beseparated in a scanning time period. In some embodiments, a motion curveof the object may be obtained. The motion curve may be associated with aphysiological motion of the object in the scanning time period. In someembodiments, position emission tomography (PET) image data of the objectgenerated in the scanning time period may be obtained. In someembodiments, one or more target motion fields corresponding to thescanning time period may be generated based on the plurality of firstsets of MR image data and the motion curve. In some embodiments, one ormore corrected PET images may be generated by correcting, based on theone or more target motion fields, the PET image data.

In some embodiments, a whole-stage motion field corresponding to theentire scanning time period may be generated based on the one or moretarget motion fields. In some embodiments, the whole-stage motion fieldmay include the one or more target motion fields. According to thepresent disclosure, the whole-stage motion field corresponding to theentire scanning time period may be generated stably through sparselyinterspersing the plurality of first sets of imaging sequences. Besides,the systems and methods may correct the motion artifact(s) in PET imagesand/or MRI images generated in the entire scanning time period using thewhole-stage motion field, and improve the image quality withoutincreasing the scanning time of the PET images and/or MRI images or withincreasing a small amount of the scanning time of the PET images and/orMRI images that hardly affects the object, and without being influencedby unstable physiological motions of the object.

FIG. 1 is a schematic diagram illustrating an exemplary image processingsystem according to some embodiments of the present disclosure. Theimage processing system 100 may include a scanner 110, a network 120,one or more terminals 130, a processing device 140, and a storage device150. The components in the image processing system 100 may be connectedin one or more of various ways. Merely by way of example, the scanner110 may be connected to the processing device 140 through the network120. As another example, the scanner 110 may be connected to theprocessing device 140 directly as indicated by the bi-directional arrowin dotted lines linking the scanner 110 and the processing device 140.As still another example, the storage device 150 may be connected to theprocessing device 140 directly or through the network 120. As stillanother example, the terminal 130 may be connected to the processingdevice 140 directly (as indicated by the bi-directional arrow in dottedlines linking the terminal 130 and the processing device 140) or throughthe network 120.

The scanner 110 may generate or provide image(s) via scanning a subjector a part of the subject. In some embodiments, the scanner 110 may be amedical imaging device, for example, a positron emission tomography(PET) device, a single-photon emission computed tomography (SPECT)device, a magnetic resonance imaging (MRI) device, or the like, or anycombination thereof. In some embodiments, the scanner 110 may include amulti-modality scanner. The multi-modality scanner may include apositron emission tomography-magnetic resonance imaging (PET-MRI)scanner, a SPET-MRI scanner, or the like, or any combination thereof.The multi-modality scanner may perform multi-modality imagingsimultaneously. For example, the PET-MRI scanner may generate MRI dataand PET data simultaneously in a single scan.

In some embodiments, the subject may include a body, substance, or thelike, or any combination thereof. In some embodiments, the subject mayinclude a specific portion of a body, such as a head, a thorax, anabdomen, or the like, or any combination thereof. In some embodiments,the subject may include a specific organ, such as a breast, a stomach, agallbladder, a small intestine, a colon, etc. In some embodiments, thesubject may include a physical model (also referred to as a mockup). Thephysical model may include one or more materials constructed asdifferent shapes and/or dimensions. Different parts of the physicalmodel may be made of different materials. Different materials may havedifferent X-ray attenuation coefficients, different tracer isotopes,and/or different hydrogen proton contents. Therefore, different parts ofthe physical model may be recognized by the image processing system 100.In the present disclosure, “object” and “subject” are usedinterchangeably. In some embodiments, the scanner 110 may include ascanning table. The subject may be placed on the scanning table forimaging.

In some embodiments, the scanner 110 may transmit the image(s) via thenetwork 120 to the processing device 140, the storage device 150, and/orthe terminal(s) 130. For example, the image(s) may be sent to theprocessing device 140 for further processing or may be stored in thestorage device 150.

The network 120 may include any suitable network that can facilitate theexchange of information and/or data for the image processing system 100.In some embodiments, one or more components of the image processingsystem 100 (e.g., the scanner 110, the terminal 130, the processingdevice 140, the storage device 150) may communicate information and/ordata with one or more other components of the image processing system100 via the network 120. For example, the processing device 140 mayobtain one or more images from the scanner 110 via the network 120. Asanother example, the processing device 140 may obtain one or more imagesfrom the storage device 150 via the network 120. As a further example,the processing device 140 may obtain user instructions from the terminal130 via the network 120. The network 120 may be and/or include a publicnetwork (e.g., the Internet), a private network (e.g., a local areanetwork (LAN), a wide area network (WAN))), a wired network (e.g., anEthernet network), a wireless network (e.g., an 802.11 network, a Wi-Finetwork), a cellular network (e.g., a Long Term Evolution (LTE)network), a frame relay network, a virtual private network (“VPN”), asatellite network, a telephone network, routers, hubs, switches, servercomputers, and/or any combination thereof. Merely by way of example, thenetwork 120 may include a cable network, a wireline network, afiber-optic network, a telecommunications network, an intranet, awireless local area network (WLAN), a metropolitan area network (MAN), apublic telephone switched network (PSTN), a Bluetooth™ network, aZigBee™ network, a near field communication (NFC) network, or the like,or any combination thereof. In some embodiments, the network 120 mayinclude one or more network access points. For example, the network 120may include wired and/or wireless network access points such as basestations and/or internet exchange points through which one or morecomponents of the image processing system 100 may be connected to thenetwork 120 to exchange data and/or information.

The terminal(s) 130 may include a mobile device 131, a tablet computer132, a laptop computer 133, or the like, or any combination thereof. Insome embodiments, the mobile device 131 may include a smart home device,a wearable device, a smart mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof.Merely by way of example, the terminal 130 may include a mobile deviceas illustrated in FIG. 3. In some embodiments, the smart home device mayinclude a smart lighting device, a control device of an intelligentelectrical apparatus, a smart monitoring device, a smart television, asmart video camera, an interphone, or the like, or any combinationthereof. In some embodiments, the wearable device may include abracelet, footwear, eyeglasses, a helmet, a watch, clothing, a backpack,a smart accessory, or the like, or any combination thereof. In someembodiments, the mobile device may include a mobile phone, a personaldigital assistant (PDA), a gaming device, a navigation device, a pointof sale (POS) device, a laptop, a tablet computer, a desktop, or thelike, or any combination thereof. In some embodiments, the virtualreality device and/or the augmented reality device may include a virtualreality helmet, virtual reality glasses, a virtual reality patch, anaugmented reality helmet, augmented reality glasses, an augmentedreality patch, or the like, or any combination thereof. For example, thevirtual reality device and/or the augmented reality device may include aGoogle Glass™, an Oculus Rift™, a Hololens™, a Gear VR™, etc. In someembodiments, the terminal(s) 130 may be part of the processing device140.

The processing device 140 may process data and/or information obtainedfrom the scanner 110, the terminal 130, and/or the storage device 150.For example, the processing device 140 may obtain a plurality of firstsets of magnetic resonance (MR) image data of an object generated basedon a plurality of first sets of imaging sequences. As another example,the processing device 140 may obtain a motion curve of the object. Asstill another example, the processing device 140 may obtain positionemission tomography (PET) image data of the object. As still anotherexample, the processing device 140 may generate one or more targetmotion fields based on the plurality of first sets of MR image data andthe motion curve. As still another example, the processing device 140may generate one or more corrected PET images by correcting, based onthe one or more target motion fields, the PET image data.

In some embodiments, the processing device 140 may be a single server ora server group. The server group may be centralized or distributed. Insome embodiments, the processing device 140 may be local or remote. Forexample, the processing device 140 may access information and/or datastored in the scanner 110, the terminal 130, and/or the storage device150 via the network 120. As another example, the processing device 140may be directly connected to the scanner 110, the terminal 130 and/orthe storage device 150 to access stored information and/or data. In someembodiments, the processing device 140 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof. In some embodiments, the processing device 140 maybe implemented by a computing device 200 having one or more componentsas illustrated in FIG. 2. In some embodiments, the processing device 140may include a central processing unit (CPU). The CPU may include asingle-core CPU, a Dual-core CPU, a Quad-core CPU, a Hex-core CPU, anOcta-core CPU, or the like, or any combination thereof.

The storage device 150 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 150 may store dataobtained from the scanner 110, the terminal 130 and/or the processingdevice 140. For example, the storage device 150 may store the pluralityof first sets of magnetic resonance (MR) image data of the objectgenerated based on the plurality of first sets of imaging sequencesobtained from the scanner 110. As another example, the storage device150 may store the one or more target motion fields generated by theprocessing device 140. In some embodiments, the storage device 150 maystore data and/or instructions that the processing device 140 mayexecute or use to perform exemplary methods described in the presentdisclosure. For example, the storage device 150 may store instructionsthat the processing device 140 may execute or use to obtain the motioncurve of the object. As another example, the storage device 150 maystore instructions that the processing device 140 may execute or use toobtain the position emission tomography (PET) image data of the object.As still another example, the storage device 150 may store instructionsthat the processing device 140 may execute or use to generate the one ormore corrected PET images based on the one or more target motion fieldsand the PET image data.

In some embodiments, the storage device 150 may include a mass storage,a removable storage, a volatile read-and-write memory, a read-onlymemory (ROM), or the like, or any combination thereof. Exemplary massstorage may include a magnetic disk, an optical disk, a solid-statedrive, etc. Exemplary removable storage may include a flash drive, afloppy disk, an optical disk, a memory card, a zip disk, a magnetictape, etc. Exemplary volatile read-and-write memory may include a randomaccess memory (RAM). Exemplary RAM may include a dynamic RAM (DRAM), adouble date rate synchronous dynamic RAM (DDR SDRAM), a static RAM(SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc.Exemplary ROM may include a mask ROM (MROM), a programmable ROM (PROM),an erasable programmable ROM (EPROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the storage device 150 maybe implemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to thenetwork 120 to communicate with one or more other components of theimage processing system 100 (e.g., the processing device 140, theterminal 130). One or more components of the image processing system 100may access the data or instructions stored in the storage device 150 viathe network 120. In some embodiments, the storage device 150 may bedirectly connected to or communicate with one or more other componentsof the image processing system 100 (e.g., the processing device 140, theterminal 130). In some embodiments, the storage device 150 may be partof the processing device 140.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device on which theprocessing device 140 may be implemented according to some embodimentsof the present disclosure. As illustrated in FIG. 2, the computingdevice 200 may include a processor 210, a storage 220, an input/output(I/O) 230, and a communication port 240.

The processor 210 may execute computer instructions (e.g., program code)and perform functions of the processing device 140 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, data structures,procedures, modules, and functions, which perform particular functionsdescribed herein. For example, the processor 210 may obtain a pluralityof first sets of magnetic resonance (MR) image data of an objectgenerated based on a plurality of first sets of imaging sequences. Asanother example, the processor 210 may obtain a motion curve of theobject. As a further example, the processor 210 may obtain positionemission tomography (PET) image data of the object. As still a furtherexample, the processor 210 may generate one or more target motion fieldscorresponding based on the plurality of first sets of MR image data andthe motion curve. As still a further example, the processor 210 maygenerate one or more corrected PET images based on the one or moretarget motion fields and the PET image data. In some embodiments, theprocessor 210 may include one or more hardware processors, such as amicrocontroller, a microprocessor, a reduced instruction set computer(RISC), an application specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operations A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the scanner110, the terminal 130, the storage device 150, and/or any othercomponent of the image processing system 100. In some embodiments, thestorage 220 may include a mass storage device, a removable storagedevice, a volatile read-and-write memory, a read-only memory (ROM), orthe like, or any combination thereof. For example, the mass storage mayinclude a magnetic disk, an optical disk, a solid-state drive, etc. Theremovable storage may include a flash drive, a floppy disk, an opticaldisk, a memory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store an image processing program forgenerating one or more target motion fields. As another example, thestorage 220 may store an image processing program for correcting imagedata (e.g., PET image data, MRI image data).

The I/O 230 may input and/or output signals, data, information, etc. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 140. In some embodiments, the I/O 230 may include aninput device and an output device. Examples of the input device mayinclude a keyboard, a mouse, a touch screen, a microphone, or the like,or a combination thereof. Examples of the output device may include adisplay device, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Examples of the display device may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), a touch screen, or the like, or a combination thereof.

The communication port 240 may be connected to a network (e.g., thenetwork 120) to facilitate data communications. The communication port240 may establish connections between the processing device 140 and thescanner 110, the terminal 130, and/or the storage device 150. Theconnection may be a wired connection, a wireless connection, any othercommunication connection that can enable data transmission and/orreception, and/or any combination of these connections. The wiredconnection may include, for example, an electrical cable, an opticalcable, a telephone wire, or the like, or any combination thereof. Thewireless connection may include, for example, a Bluetooth™ link, aWi-Fi™ link, a WiMax™ link, a WLAN link, a ZigBee link, a mobile networklink (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof. Insome embodiments, the communication port 240 may be and/or include astandardized communication port, such as RS232, RS485, etc. In someembodiments, the communication port 240 may be a specially designedcommunication port. For example, the communication port 240 may bedesigned in accordance with the digital imaging and communications inmedicine (DICOM) protocol.

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which theterminal 130 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 3, the mobile device 300 mayinclude a communication unit 310, a display 320, a graphics processingunit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, amemory 360, and a storage 390. In some embodiments, any other suitablecomponent, including but not limited to a system bus or a controller(not shown), may also be included in the mobile device 300. In someembodiments, a mobile operating system (OS) 370 (e.g., iOS™, Android™,Windows Phone™, etc.) and one or more applications (App(s)) 380 may beloaded into the memory 360 from the storage 390 in order to be executedby the CPU 340. The applications 380 may include a browser or any othersuitable mobile apps for receiving and rendering information relating toimage processing or other information from the processing device 140.User interactions with the information stream may be achieved via theI/O 350 and provided to the processing device 140 and/or othercomponents of the image processing system 100 via the network 120. Insome embodiments, a user may input parameters to the image processingsystem 100, via the mobile device 300.

In order to implement various modules, units and their functionsdescribed above, a computer hardware platform may be used as hardwareplatforms of one or more elements (e.g., the processing device 140and/or other components of the image processing system 100 described inFIG. 1). Since these hardware elements, operating systems and programlanguages are common; it may be assumed that persons skilled in the artmay be familiar with these techniques and they may be able to provideinformation needed in the image processing operations according to thetechniques described in the present disclosure. A computer with the userinterface may be used as a personal computer (PC), or other types ofworkstations or terminal devices. After being properly programmed, acomputer with the user interface may be used as a server. It may beconsidered that those skilled in the art may also be familiar with suchstructures, programs, or general operations of this type of computingdevice.

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure. As shown inFIG. 4, the processing device 140 may include an obtaining module 410, ageneration module 420, and a correction module 430.

In some embodiments, the obtaining module 410 may be configured toobtain image data, a motion curve, or the like, or a combinationthereof. In some embodiments, the obtaining module 410 may include animage data obtaining unit 412 and a motion curve obtaining unit 414. Insome embodiments, the image data obtaining unit 412 may obtain aplurality of first sets of magnetic resonance (MR) image data of anobject. In some embodiments, the image data obtaining unit 412 mayobtain position emission tomography (PET) image data of an objectgenerated in a scanning time period. The PET image data may include PETraw data or data corresponding to one or more PET images reconstructedbased on the PET raw data. In some embodiments, the image data obtainingunit 412 may obtain a plurality of second sets of MR image data of theobject. In some embodiments, the motion curve obtaining unit 414 mayobtain a motion curve of the object. More descriptions of the image dataobtaining unit 412 and the motion curve obtaining unit 414 may be foundelsewhere in the present disclosure (e.g., FIG. 5 and descriptionsthereof).

In some embodiments, the generation module 420 may be configured togenerate one or more motion fields. In some embodiments, the generationmodule 420 may include a first motion field generation unit 422, asecond motion field generation unit 424, and a target motion fieldgeneration unit 426.

In some embodiments, the first motion field generation unit 422 maydetermine a plurality of respiratory phases of a respiratory motion ofthe object. In some embodiments, the first motion field generation unit422 may determine a plurality of pieces of MR image data correspondingto the plurality of respiratory phases. In some embodiments, the firstmotion field generation unit 422 may reconstruct a plurality of imagescorresponding to the plurality of respiratory phases based on theplurality of pieces of MR image data. In some embodiments, the firstmotion field generation unit 422 may generate at least one first set ofmotion fields based on the plurality of images corresponding to theplurality of respiratory phases. In some embodiments, the first motionfield generation unit 422 may determine a plurality of first sets oftime intervals in which a plurality of first sets of MR image data aregenerated. In some embodiments, the first motion field generation unit422 may determine at least one portion of the respiratory motion curvecorresponding to the plurality of first sets of time intervals. In someembodiments, the first motion field generation unit 422 may determine aplurality of respiratory phases of the respiratory motion of the objectbased on the at least one portion of the respiratory motion curve.

In some embodiments, the second motion field generation unit 424 maydetermine a plurality of second sets of time intervals. In someembodiments, the second motion field generation unit 424 may obtain aplurality of second sets of motion fields based on the plurality offirst sets of motion fields. In some embodiments, the second motionfield generation unit 424 may obtain the second sets of motion fields bygenerating, based on the plurality of first sets of motion fields, atleast one second set of motion fields corresponding to each second setof time intervals of the plurality of second sets of time intervals.

In some embodiments, the target motion field generation unit 426 maygenerate one or more target motion fields corresponding to the scanningtime period based on the plurality of first sets of MR image data andthe motion curve. In some embodiments, the target motion fieldgeneration unit 426 may generate one or more target motion fields basedon the plurality of first sets of motion fields. In some embodiments,the target motion field generation unit 426 may generate at least aportion of the target motion fields by designating the first sets ofmotion fields as target motion fields. In some embodiments, the targetmotion field generation unit 426 may generate at least a portion of thetarget motion fields by duplicating the first sets of motion fields. Insome embodiments, the target motion field generation unit 426 maygenerate at least a portion of the target motion fields by fitting thefirst sets of motion fields. In some embodiments, the target motionfield generation unit 426 may generate the one or more target motionfields based on the plurality of first sets of motion fields and theplurality of second sets of motion fields. In some embodiments, thetarget motion field generation unit 426 may designate the plurality offirst sets of motion fields and the plurality of second sets of motionfields as the one or more target motion fields.

In some embodiments, the correction module 430 may correct the PET imagedata and/or generate one or more corrected PET images based on thetarget motion fields and the PET image data. In some embodiments, thecorrection module 430 may generate the corrected PET images bycorrecting, based on the one or more target motion fields, the PET imagedata. In some embodiments, the correction module 430 may correct thesecond sets of MR image data and/or generate one or more corrected MRimages based on the target motion fields and the second sets of MR imagedata.

It should be noted that the above description is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure. For example, theprocessing device 140 may further include a storage module (not shown inFIG. 4). The storage module may be configured to store data (e.g., theMR image data, the PET image data, the motion curve, the one or moretarget motion fields, the one or more corrected PET images, etc.)obtained and/or generated during any process performed by any componentof the processing device 140. As another example, each of components ofthe processing device 140 may include a storage device. Additionally oralternatively, the components of the processing device 140 may share astorage device.

FIG. 5 is a flowchart illustrating an exemplary process for imageprocessing according to some embodiments of the present disclosure. Insome embodiments, process 500 may be executed by the image processingsystem 100. For example, the process 500 may be implemented as a set ofinstructions (e.g., an application) stored in one or more storagedevices (e.g., the storage device 150, the storage 220, and/or thestorage 390) and invoked and/or executed by the processing device 140(implemented on, for example, the processor 210 of the computing device200, and the CPU 340 of the mobile device 300). The operations of theprocess 500 presented below are intended to be illustrative. In someembodiments, the process 500 may be accomplished with one or moreadditional operations not described, and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 500 as illustrated in FIG. 5 and described below is notintended to be limiting.

In 502, the processing device 140 (e.g., the obtaining module 410, orthe image data obtaining unit 412) may obtain a plurality of first setsof magnetic resonance (MR) image data of an object. In some embodiments,the first sets of MR image data may be obtained from the scanner 110.Alternatively or additionally, the first sets of MR image data may beobtained from the storage device 150 or an external data source. In someembodiments, a (or each) first set of MR image data may be used toreconstruct one or more MR images. Accordingly, the plurality of firstsets of MR image data may be used to reconstruct a plurality of MRimages. As shown in FIG. 9, the plurality of first sets of MR image data(e.g., the MR image data represented by the regions A, C, and/or E ofthe band 903) may be obtained.

In some embodiments, the plurality of first sets of MR image data may begenerated based on a plurality of first sets of imaging sequences. Afirst set of MR image data may refer to image data generated based on afirst set of imaging sequences. An imaging sequence may refer to orinclude a setting of one or more pulse sequences and/or one or morepulsed field gradients. The imaging sequence(s) may be used to scan theobject and/or generate MR image data. The imaging sequence(s) mayinclude or be associated with one or more parameters (e.g., aradio-frequency (RF) pulse, a gradient field, data acquisition time,etc.) and an arrangement thereof in sequence(s). Exemplary imagingsequences may include a free inductive decay (FID) sequence, a spin echo(SE) sequence, an inversion recovery (IR) sequence, a gradient echo(GRE) sequence, an echo planar imaging (EPI), or the like, or acombination thereof. In some embodiments, the imaging sequence(s) may bedetermined according to a scanning protocol. The scanning protocol mayinclude imaging sequence(s) and scanning parameter(s). In someembodiments, the scanning protocol may be obtained from the storagedevice 150 or an external data source. In some embodiments, the scanningprotocol may be provided by a user (e.g., a doctor, a technician, aphysician, an engineer, etc.). In some embodiments, the scanningprotocol may be generated automatically, for example, according to amachine learning model.

A first set of imaging sequences may be a special sequence used to scanthe object and/or obtain motion field information associated with theobject. In some embodiments, the first set of imaging sequences may bedifferent from a general sequence that is used to scan the object and/orobtain image data of the object. Exemplary first sets of imagingsequences may include a multi-cycle radial imaging sequence, a spiralimaging sequence, a random imaging sequence, a radial imaging sequencewith a golden-angle scheme, or the like, or a combination thereof. Insome embodiments, the first set of imaging sequences may last arelatively short time period, for example, one or more (e.g., two orthree) respiratory cycles. Accordingly, a first set of MR image datagenerated based on the first set of imaging sequences may correspond toimage data generated in the one or more respiratory cycles. In someembodiments, the plurality of first sets of imaging sequences may beseparated in a scanning time period. As used herein, the scanning timeperiod may refer to or include an entire scanning time period in whichthe first sets of MR image data (as illustrated in 502), the PET imagedata (as illustrated in 508), and/or the second sets of MR image data(as illustrated in 512) are generated. In some embodiments, each firstset of imaging sequences may be separated from another first set ofimaging sequences in time.

In 504, the processing device 140 (e.g., the obtaining module 410, orthe motion curve obtaining unit 414) may obtain a motion curve of theobject. In some embodiments, the motion curve may be obtained from thescanner 110. In some embodiments, the motion curve may be obtained froma motion detection device associated with or external to the imageprocessing system 100. Alternatively or additionally, the motion curvemay be obtained from the storage device 150 or an external data source.

The motion curve may express a physiological motion of the object in thescanning time period. As shown in FIG. 9, the waveform 901 may indicatean exemplary motion curve of an object collected during a scanning timeperiod. In some embodiments, the scanning time period may be relativelylong (e.g., the scanning time period may include multiple respiratorycycles or phases), and it may be difficult for the object to keep in astatic state, or maintain a same physiological motion state during thescanning time period. Data associated with the multiple respiratorycycles or phases may be recorded during the scanning time period toobtain the motion curve. In some embodiments, the processing device 140(e.g., the obtaining module 410) may obtain the motion curve of theobject when the first sets of MR image data are generated based on theplurality of first sets of imaging sequences. In some embodiments, thegeneration of at least a portion of the motion curve and the generationof the first sets of MR image data may be performed simultaneously orsynchronously.

In some embodiments, the motion curve may include a respiratory motioncurve, a cardiac motion curve, or the like, or any combination thereof.In some embodiments, the motion curve may be obtained or generated byrecording physiological motion data (e.g., using a motion detectiondevice) during the scanning time period. Exemplary motion detectiondevices may include an abdominal band, a camera, a contactless sensingdevice, a vital sign data acquisition device, or the like, or acombination thereof. In some embodiments, the motion detection devicemay be integrated into the image processing system 100 (e.g., thescanner 110). In some embodiments, the motion detection device may beexternal to the image processing system 100 (e.g., the motion detectiondevice may not be integrated into the scanner 110). For example, anabdominal band may be worn (e.g., before the object is scanned) on theabdomen of the object to collect respiratory signals during the scanningtime period, and then the collected respiratory signals may be processedto obtain the respiratory motion curve. As another example, the scanner110 (e.g., an MRI scanner) may be equipped with a camera that may beused to capture images of the object during the scanning time period,and the images may be processed (e.g., data of region(s) of interest(e.g., a chest, an abdomen, etc.) may be extracted from the images,and/or the extracted data may be further processed) to obtain therespiratory motion curve. As a further example, one or more contactlesssensing devices (e.g., a radar, a coil, etc.) may be disposed in thescanner 110 to collect respiratory signals of the object. As still afurther example, a vital sign data acquisition device may be disposed inthe scanner 110 to generate or obtain a physiological motion curve.Exemplary vital sign data acquisition devices may include a vital signdata acquisition device based on an electromagnetic echo signal, a vitalsign data acquisition device based on an electrocardiograph (ECG)signal, a vital sign data acquisition device based on a photoelectricsignal, a vital signs data acquisition device based on a pressureoscillation signal, or the like, or any combination thereof.

In some embodiments, if a PET-MRI scanner is used, the motion curve maybe obtained by processing PET data generated during the scanning timeperiod. In some embodiments, the respiratory motion curve may beobtained or generated by extracting data relating to the physiologicalmotion of the object based on imaging data (e.g., raw data, image data)collected by the MRI scanner, and processing the data. It should benoted that the obtaining of the motion curve illustrated above is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure.

In 506, the processing device 140 (e.g., the generation module 420, orthe target motion field generation unit 426) may generate one or moretarget motion fields corresponding to the scanning time period based onthe plurality of first sets of MR image data and the motion curve.

A motion field may refer to a function that maps or registers two ormore images (e.g., images generated in different physiological motionstates (e.g., different respiratory cycles or phases) of the object).For example, a first image generated in a physiological motion stateS_(A) may be registered, based on a motion field, with a second imagegenerated in a physiological motion state S_(B). Alternatively oradditionally, by using the motion field to register the first image withthe second image, a (or each) first pixel or voxel in the first imagemay be moved to a position of a corresponding second pixel or voxel inthe second image, in which the first pixel or voxel and thecorresponding second pixel or voxel may represent a same portion of theobject. In some embodiments, if the first image is processed based onthe motion field, the processed first image may be substantiallyconsistent with the second image.

The target motion fields may refer to motion fields corresponding to thescanning time period. In some embodiments, a (or each) target motionfield may correspond to a specific time point or time period within thescanning time period, or a (or each) target motion field may correspondto a specific respiratory cycle or phase (or a specific respiratoryphase of a specific respiratory cycle) within the scanning time period,and accordingly, the scanning time period may correspond to multipletarget motion fields. The target motion fields may be further used tocorrect image data (e.g., MR image data, PET image data) and/or removeor eliminate motion artifacts in images corresponding to the image data.In some embodiments, the one or more target motion fields correspondingto the scanning time period may be generated based on the plurality offirst sets of MR image data and the motion curve. More descriptions ofthe generation of the target motion fields may be found elsewhere in thepresent disclosure (e.g., FIGS. 6-8 and descriptions thereof).

In 508, the processing device 140 (e.g., the obtaining module 410, orthe image data obtaining unit 412) may obtain position emissiontomography (PET) image data of the object generated in the scanning timeperiod. As shown in FIG. 9, the band 902 may indicate PET image datagenerated in the scanning time period. The PET image data may includePET raw data or data corresponding to one or more PET imagesreconstructed based on the PET raw data. In some embodiments, thegeneration of at least a portion of the PET image data may besimultaneous or synchronous to the generation of the plurality of firstsets of MR image data. That is, an image reconstructed by a first set ofMR image data may correspond to an image reconstructed based on at leasta portion of the PET image data. In some embodiments, the generation ofthe motion curve and the generation of the PET image data may beperformed simultaneously or synchronously. For example, the scanner 110may be a PET-MRI scanner, and during PET scanning (or during thegeneration of the PET image data), the MR scanning may be performed (orthe first sets of MR image data may be generated) simultaneously orsynchronously, and the motion signal collection may also be performedsimultaneously or synchronously.

In 510, the processing device 140 (e.g., the correction module 430) maycorrect the PET image data and/or generate one or more corrected PETimages based on the target motion fields and the PET image data. In someembodiments, the processing 140 may generate the corrected PET images bycorrecting, based on the one or more target motion fields, the PET imagedata. According to operations 502 through 510, generally, the PET imagedata may be corrected based on the first sets of MR image data and themotion curve. As shown in FIG. 9, the PET image data (e.g., the PETimage data represented by the band 902) may be corrected based on theplurality of first sets of MR image data (e.g., the MR image datarepresented by the regions A, C, and/or E of the band 903).

In some embodiments, a pre-reconstruction or a post-reconstructionoperation may be performed to correct the PET image data. Thepre-reconstruction operation may refer to a correction operation on thePET raw data followed by an image reconstruction operation on thecorrected PET raw data. In some embodiments, after correcting the PETraw data based on the one or more target motion fields, the correctedPET raw data may be used for image reconstruction to obtain correctedPET images including no or reduced motion artifact(s). Thepost-reconstruction operation may refer to an image reconstructionoperation on the PET raw data followed by a correction operation on thereconstructed PET images. In some embodiments, after the PET raw dataare used for image reconstruction to obtain PET images, the PET imagesmay be corrected, based on the one or more target motion fields, toobtain corrected PET images including no or reduced motion artifact(s).In some embodiments, a (or each) piece of PET raw data or a (or each)corresponding reconstructed PET image that is generated at a specifictime point or time period may correspond to a target motion field withrespect to the specific time point or time period. In some embodiments,the PET raw data or the reconstructed PET images may be multiplied bycorresponding target motion fields to obtain corrected PET images.

In 512, the processing device 140 (e.g., the obtaining module 410, orthe image data obtaining unit 412) may obtain a plurality of second setsof MR image data of the object. In some embodiments, the second sets ofMR image data may be obtained from the scanner 110. Alternatively oradditionally, the second sets of MR image data may be obtained from thestorage device 150 or an external data source. In some embodiments, a(or each) second set of MR image data may be used to reconstruct one ormore MR images. Accordingly, the plurality of second sets of MR imagedata may be used to reconstruct a plurality of MR images. As shown inFIG. 9, the plurality of second sets of MR image data (e.g., the MRimage data represented by the regions B, D, and/or F of the band 903)may be obtained. The plurality of second sets of MR image data mayinclude MR raw data or data corresponding to one or more MR imagesreconstructed based on the MR raw data.

In some embodiments, the plurality of second sets of MR image data maybe generated based on a plurality of second sets of imaging sequences. Asecond set of MR image data may refer to image data generated based on asecond set of imaging sequences. In some embodiments, a second set ofimaging sequences may be a general set of sequences that is used to scanthe object and/or obtain image data of the object. The second sets ofimaging sequences may be different from the first sets of imagingsequences. Exemplary second sets of imaging sequences may include a freeinductive decay (FID) sequence, a spin echo (SE) sequence, an inversionrecovery (IR) sequence, a gradient echo (GRE) sequence, an echo planarimaging (EPI) sequence, or the like, or a combination thereof. In someembodiments, as described in 502, the first set of imaging sequences maylast a relatively short time period, for example, one or more (e.g., twoor three) respiratory cycles. In some embodiments, the second set ofimaging sequences may last a relatively long time period (e.g., one ormore minutes) with respect to the first set of imaging sequences.Accordingly, a second set of MR image data generated based on the secondset of imaging sequences may correspond to image data generated in therelatively long time period. In some embodiments, the second sets of MRimage data may include clinical information of the object and/or be usedto reconstruct clinical images of the object, while the first sets of MRimage data may be used to obtain motion field information associatedwith the object.

In some embodiments, as described in 502, the plurality of first sets ofimaging sequences may be separated in the entire scanning time period.In some embodiments, the plurality of second sets of imaging sequencesmay be interleaved with the plurality of first sets of imagingsequences. For example, two adjacent second sets of imaging sequencesmay be spaced apart by a first set of imaging sequences, or two adjacentfirst sets of imaging sequences may be spaced apart by one or moresecond sets of imaging sequences. In some embodiments, the entirescanning time period may include a first plurality of time periodscorresponding to the first sets of imaging sequences and a secondplurality of time periods corresponding to the second sets of imagingsequences. In some embodiments, the distribution of the first sets ofimaging sequences and/or the second sets of imaging sequences throughthe entire scanning time period may be ununiform. In some embodiments,the plurality of first sets of imaging sequences may be sparselyinterspersed between the plurality of second sets of imaging sequences.For example, each two adjacent first sets of imaging sequences may bespaced apart by one or more second sets of imaging sequences.Accordingly, the second plurality of time periods corresponding to thesecond sets of imaging sequences may be longer than the first pluralityof time periods corresponding to the first sets of imaging sequences.Further, the number or count of the second plurality of time periodscorresponding to the second sets of imaging sequences may be larger thanthe number or count of the first plurality of time periods correspondingto the first sets of imaging sequences. For example, if the entirescanning time period lasts 20 minutes, the first plurality of timeperiods corresponding to the first sets of imaging sequences may be 1minute, while the second plurality of time periods corresponding to thesecond sets of imaging sequences may be 19 minutes. In some embodiments,within the entire scanning time period, a time delay may be presentbetween two successive sets of imaging sequences (e.g., a first set ofimaging sequences and a successive second set of imaging sequences, asecond set of imaging sequences and a successive first set of imagingsequences, two successive second sets of imaging sequences, or thelike). In some embodiments, within the entire scanning time period, notime delay may be present between two successive sets of imagingsequences.

In some embodiments, the plurality of first sets of imaging sequencesmay be only interspersed between a portion of the second sets of imagingsequences (e.g., the second sets of imaging sequences used to scan anabdomen, a breast, a chest, or the like). Therefore, the scanner 110 mayscan the object in time and efficiently, and the processing device 140(e.g., the obtaining module 410) may efficiently obtain the plurality offirst sets of MR image data of the object generated based on theplurality of first sets of imaging sequences and the plurality of secondsets of MR image data of the object generated based on the plurality ofsecond sets of imaging sequences. Through the sparsely interspersing ofthe plurality of first sets of imaging sequences, relatively smallamount of first sets of MR image data of the object may be obtained togenerate the target motion fields, and sufficient second sets of MRimage data of the object may be obtained to reconstruct diagnosis imagesof the object, thereby ensuring image qualities and improving imageacquisition efficiencies.

In some embodiments, the generation of at least a portion of the PETimage data (illustrated in 508) may be simultaneous or synchronous tothe generation of the plurality of second sets of MR image data. Thatis, an image reconstructed by a second set of MR image data maycorrespond to an image reconstructed based on at least a portion of thePET image data. In some embodiments, the generation or collection of atleast a portion of the motion curve (illustrated in 504) may besimultaneous or synchronous to the generation of the plurality of secondsets of MR image data.

In 514, the processing device 140 (e.g., the correction module 430) maycorrect the second sets of MR image data and/or generate one or morecorrected MR images based on the target motion fields and the secondsets of MR image data. In some embodiments, the one or more corrected MRimages may be generated by correcting, based on the one or more targetmotion fields, the plurality of second sets of MR image data. Accordingto operations 502 through 514, generally, the second sets of MR imagedata may be corrected based on the first sets of MR image data and themotion curve. As shown in FIG. 9, the second sets of MR image data(e.g., the MR image data represented by the regions B, D, and/or F ofthe band 903) may be corrected based on the plurality of first sets ofMR image data (e.g., the MR image data represented by the regions A, C,and/or E of the band 903).

In some embodiments, a pre-reconstruction or a post-reconstructionoperation may be performed to correct the second sets of MR image data.The pre-reconstruction operation may refer to a correction operation onthe MR raw data followed by an image reconstruction operation on thecorrected MR raw data. In some embodiments, after correcting the MR rawdata based on the one or more target motion fields, the corrected MR rawdata may be used for image reconstruction to obtain corrected MR imagesincluding no or reduced motion artifact(s). The post-reconstructionoperation may refer to an image reconstruction operation on the MR rawdata followed by a correction operation on the reconstructed MR images.In some embodiments, after the MR raw data are used for imagereconstruction to obtain MR images, the MR images may be corrected,based on the one or more target motion fields, to obtain corrected MRimages including no or reduced motion artifact(s). In some embodiments,a (or each) piece of MR raw data or a (or each) correspondingreconstructed MR image that is generated at a specific time point ortime period may correspond to a target motion field with respect to thespecific time point or time period. In some embodiments, the MR raw dataor the reconstructed MR images may be multiplied by corresponding targetmotion fields to obtain corrected MR images.

Referring to FIG. 9, FIG. 9 is a schematic diagram illustrating anexemplary process for generating target motion fields and correctingimage data based on the target motion fields according to someembodiments of the present disclosure. The waveform 901 may indicate anexemplary motion curve of an object collected during a scanning timeperiod. The band 902 may indicate PET image data generated in thescanning time period. The band 903 may indicate MR image data generatedin the scanning time period. The regions A, C, and/or E of the band 903may represent the first sets of MR image data generated based on thefirst sets of imaging sequences, while the regions B, D, and/or F of theband 903 may represent the second sets of MR image data generated basedon the second sets of imaging sequences, in which the first sets ofimaging sequences may be sparsely interspersed between the second setsof imaging sequences. The first sets of MR image data (represented bythe regions A, C, and/or E of the band 903) and the motion curve of theobject (represented by the waveform 901) may be used to generate targetmotion fields, and the target motion fields may be further used tocorrect the second sets of MR image data (represented by the regions B,D, and/or F of the band 903) and/or the PET image data (represented bythe band 902) to generate corrected MR images and/or corrected PETimages including no or reduced motion artifact(s). It should be notedthat in some embodiments, the use of the second sets of imagingsequences is not necessary. That is, within the entire scanning timeperiod, only first sets of imaging sequences may be used sparsely, andaccordingly, only first sets of MR image data may be generated, whilethe second sets of imaging sequences may not be used, and the secondsets of MR image data may not be generated. In such cases, the regionsB, D, and/or F of the band 903 may be blank, and the first sets of MRimage data may only be used to correct the PET image data. In someembodiments, the first sets of MR image data (or the second sets of MRimage data), and the PET image data may be collected simultaneously orsynchronously by a PET-MRI scanner. In some embodiments, the second setsof MR image data and the PET image data may be corrected as illustratedabove, respectively. In some embodiments, the corrected MR images andthe corrected PET images may be registered and/or fused to obtainmulti-modality images to facilitate image analyses and/or diseasediagnoses.

It should be noted that the above description of process 500 is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, operation 504 may be performed before or simultaneouslywith operation 502. As another example, operation 508 may be performedbefore or simultaneously with operations 502 and/or 504. As stillanother example, one or more of operations 508 through 514 may beomitted. As a further example, operations 502 and 512 may be performedalternately.

FIG. 6 is a flowchart illustrating an exemplary process for generatingtarget motion fields according to some embodiments of the presentdisclosure. In some embodiments, at least part of process 600 may beperformed by the processing device 140 (implemented in, for example, thecomputing device 200 shown in FIG. 2). For example, the process 600 maybe stored in a storage device (e.g., the storage device 150, the storage220, the storage 390) in the form of instructions (e.g., anapplication), and invoked and/or executed by the processing device 140(e.g., the processor 210 illustrated in FIG. 2, the CPU 340 illustratedin FIG. 3, or one or more modules in the processing device 140illustrated in FIG. 4). The operations of the illustrated processpresented below are intended to be illustrative. In some embodiments,the process 600 may be accomplished with one or more additionaloperations not described, and/or without one or more of the operationsdiscussed. Additionally, the order in which the operations of theprocess 600 as illustrated in FIG. 6 and described below is not intendedto be limiting. In some embodiments, operation 506 illustrated in FIG. 5may be performed according to the process 600.

In 602, the processing device 140 (e.g., the generation module 420, orthe first motion field generation unit 422) may determine a plurality ofrespiratory phases of a respiratory motion of the object. In someembodiments, the respiratory motion of the object may include aplurality of respiratory cycles. A (or each) respiratory cycle mayinclude one or more respiratory phases. A respiratory phase maycorrespond to or indicate a specific respiratory state of the object.Exemplary respiratory phases in a respiratory cycle may include aninitial stage of inspiration, an end stage of inspiration, an initialstage of expiration, an end stage of expiration, etc. In someembodiments, different respiratory cycles may have the same respiratoryphases. For example, a first respiratory cycle may include an initialstage of inspiration, an end stage of inspiration, an initial stage ofexpiration, and an end stage of expiration, and a second respiratorycycle may also include an initial stage of inspiration, an end stage ofinspiration, an initial stage of expiration, and an end stage ofexpiration. It should be noted that the above exemplary respiratoryphases are merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. In someembodiments, the plurality of respiratory phases of the respiratorymotion of the object may be determined based on at least one portion ofa respiratory motion curve of the object. More descriptions of thedetermination of the plurality of respiratory phases of the respiratorymotion of the object may be found elsewhere in the present disclosure(e.g., FIG. 7 and descriptions thereof).

In 604, the processing device 140 (e.g., the generation module 420, orthe first motion field generation unit 422) may determine a plurality ofpieces of MR image data corresponding to the plurality of respiratoryphases. A (or each) piece of MR image data may correspond to arespiratory phase. In some embodiments, as illustrated in FIG. 5, ageneration of at least a portion of the respiratory motion curve may besimultaneous or synchronous to a generation of the plurality of firstsets of MR image data, each first set of MR image data generated basedon a first set of imaging sequences may correspond to one or morerespiratory cycles, and each respiratory cycle may include one or morerespiratory phases. In some embodiments, the plurality of pieces of MRimage data may be determined by determining, based on the plurality offirst sets of MR image data and at least a portion of the respiratorymotion curve, a piece of MR image data corresponding to each of theplurality of respiratory phases. Specifically, the plurality of firstsets of MR image data may be divided into the plurality of pieces of MRimage data corresponding to the plurality of respiratory phases, basedon the plurality of first sets of MR image data and the plurality ofrespiratory phases. In some embodiments, a (or each) first set of MRimage data may be divided into multiple pieces of MR image datacorresponding to multiple respiratory phases. Merely by way of example,if a first set of imaging sequences corresponds to two respiratorycycles, each respiratory cycle includes four respiratory phases (i.e.,the first set of imaging sequences corresponds to eight respiratoryphases), then a first set of MR image data generated based on the firstset of imaging sequences may correspond to eight respiratory phases, andthe first set of MR image data may be divided into eight pieces of MRimage data corresponding to the eight respiratory phases. In someembodiments, a (or each) respiratory phase may have a timestamp (e.g., arecorded time point or time period) correlative to the entire scanningtime period, and the MR image data collected by the scanner 110 may alsohave timestamps (e.g., recorded time points or time periods) correlativeto the entire scanning time period, and thus, the first set(s) of MRimage data may be divided, based on the timestamp(s) of the respiratoryphase(s) and the timestamp(s) of the first set(s) of MR image data, toobtain the piece(s) of MR image data.

In 606, the processing device 140 (e.g., the generation module 420, orthe first motion field generation unit 422) may reconstruct a pluralityof images corresponding to the plurality of respiratory phases based onthe plurality of pieces of MR image data. The images may bereconstructed using one or more reconstruction algorithms. Exemplaryreconstruction algorithms may include a rapid reconstruction, analgebraic reconstruction, an iteration reconstruction, a back projectionreconstruction, or the like, or any combination thereof. Exemplary rapidreconstruction algorithms may include fast Fourier transform, acompressed sensing algorithm, a deep learning algorithm, or the like, orany combination thereof. In some embodiments, the plurality ofreconstructed images corresponding to the plurality of respiratoryphases may include no or reduced motion artifact(s). In someembodiments, motion correction may be performed before, during, or afterimage reconstruction to obtain images including no or reduced motionartifact(s). The motion correction may include pre-reconstruction,post-reconstruction, or the combination thereof. If pre-reconstructionis performed, raw data corresponding to the pieces of MR image data maybe corrected and then reconstructed to obtain images including no orreduced motion artifact(s). If post-reconstruction is performed, rawdata corresponding to the pieces of MR image data may be reconstructedto obtain images (including motion artifact(s)) and then be corrected toobtain images including no or reduced motion artifact(s). In someembodiments, a (or each) piece of MR image data corresponding to arespiratory phase may be used to reconstruct an image including no orreduced motion artifact(s). In some embodiments, two or more pieces ofMR images data corresponding to a same respiratory phase in differentrespiratory cycles may be used to reconstruct an image including no orreduced motion artifact(s).

In 608, the processing device 140 (e.g., the generation module 420, orthe first motion field generation unit 422) may generate at least onefirst set(s) of motion fields based on the plurality of imagescorresponding to the plurality of respiratory phases. A first set ofmotion fields may refer to a set of motion fields derived from the firstset(s) of MR image data generated based on the first set(s) of imagingsequences. A motion field may refer to a function that maps or registersa first image corresponding to a respiratory phase A with a second image(also referred to as a reference image) corresponding to a respiratoryphase B (e.g., a reference respiratory phase (e.g., a resting period ofan end stage of expiration)). More descriptions of the motion field maybe found elsewhere in the present disclosure (e.g., FIG. 5 anddescriptions thereof). In some embodiments, a (or each) first set ofmotion fields may correspond to a respiratory phase. In someembodiments, one or more first sets of motion fields (or at least onefirst set of motion fields) may correspond to a first set of timeintervals in which one first set of MR image data are generated. In someembodiments, a plurality of first sets of motion fields may be obtainedby generating, based on the motion curve, at least one first set ofmotion fields corresponding to each first set of time intervals in whichone first set of MR image data among the plurality of first sets of MRimage data are generated. In some embodiments, a (or each) first set ofmotion fields may be generated by registering a correspondingreconstructed image with a reference image.

In some embodiments, an image corresponding to a specific respiratoryphase of a specific respiratory cycle may be used as a reference imagefor all respiratory cycles. Merely by way of example, if a first set oftime intervals includes two respiratory cycles (e.g., a firstrespiratory cycle, a second respiratory cycle), a respiratory cycleincludes four respiratory phases (e.g., a first respiratory phase, asecond respiratory phase, a third respiratory phase, and a fourthrespiratory phase), eight images (e.g., a first image corresponding tothe first respiratory phase of the first respiratory cycle, a secondimage corresponding to the second respiratory phase of the firstrespiratory cycle, a third image corresponding to the third respiratoryphase of the first respiratory cycle, a fourth image corresponding tothe fourth respiratory phase of the first respiratory cycle, a fifthimage corresponding to the first respiratory phase of the secondrespiratory cycle, a sixth image corresponding to the second respiratoryphase of the second respiratory cycle, a seventh image corresponding tothe third respiratory phase of the second respiratory cycle, and aneighth image corresponding to the fourth respiratory phase of the secondrespiratory cycle) including no or reduced motion artifact(s) arereconstructed, and the first image corresponding to the firstrespiratory phase of the first respiratory cycle is used as thereference image for all respiratory cycles, then seven first sets ofmotion fields corresponding to the first sets of time intervals may begenerated (e.g., a first set of motion fields M₁ may be generated byregistering the second image with the first image, a second set ofmotion fields M₂ may be generated by registering the third image withthe first image, a third motion field M₃ may be generated by registeringthe fourth image with the first image, a fourth motion field M₄ may begenerated by registering the fifth image with the first image, a fifthmotion field M₅ may be generated by registering the sixth image with thefirst image, a sixth motion field M₆ may be generated by registering theseventh image with the first image, a seventh motion field M₇ may begenerated by registering the eighth image with the first image).

In some embodiments, an image corresponding to a specific respiratoryphase of a current respiratory cycle may be used as a reference imagefor the current respiratory cycles. Merely by way of example, asillustrated above, if the first image corresponding to the firstrespiratory phase of the first respiratory cycle is used as thereference image for the first respiratory cycle, and the fifth imagecorresponding to the first respiratory phase of the second respiratorycycle is used as the reference image for the second respiratory cycle,then six first sets of motion fields corresponding to the first set oftime intervals may be generated (e.g., a first set of motion fields M₁may be generated by registering the second image with the first image, asecond set of motion fields M₂ may be generated by registering the thirdimage with the first image, a third set of motion fields M₃ may begenerated by registering the fourth image with the first image, a fourthset of motion fields M₄ may be generated by registering the sixth imagewith the fifth image, a fifth set of motion fields M₅ may be generatedby registering the seventh image with the fifth image, a sixth set ofmotion fields M₆ may be generated by registering the eighth image withthe fifth image).

In 610, the processing device 140 (e.g., the generation module 420, orthe target motion field generation unit 426) may generate one or moretarget motion fields based on the plurality of first sets of motionfields. In some embodiments, at least a portion of the target motionfields may be generated by designating the first sets of motion fieldsas target motion fields. In some embodiments, at least a portion of thetarget motion fields may be generated by duplicating the first sets ofmotion fields. In some embodiments, at least a portion of the targetmotion fields may be generated by fitting the first sets of motionfields. More descriptions of the generation of the target motion fieldsmay be found elsewhere in the present disclosure (e.g., FIG. 8 anddescriptions thereof).

It should be noted that the above description of process 600 is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, operations 602 and 604 may be integrated into a singleoperation.

FIG. 7 is a flowchart illustrating an exemplary process for determininga plurality of respiratory phases of a respiratory motion of an objectaccording to some embodiments of the present disclosure. In someembodiments, at least part of process 700 may be performed by theprocessing device 140 (implemented in, for example, the computing device200 shown in FIG. 2). For example, the process 700 may be stored in astorage device (e.g., the storage device 150, the storage 220, thestorage 390) in the form of instructions (e.g., an application), andinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 illustrated in FIG. 2, the CPU 340 illustrated in FIG. 3,or one or more modules in the processing device 140 illustrated in FIG.4). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 700 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 800 as illustrated inFIG. 7 and described below is not intended to be limiting. In someembodiments, operation 602 illustrated in FIG. 6 may be performedaccording to the process 700.

In 702, the processing device 140 (e.g., the generation module 420, orthe first motion field generation unit 422) may determine a plurality offirst sets of time intervals in which a plurality of first sets of MRimage data are generated. A first set of time intervals may refer to atime period in which one first set of MR image data are generated. Thefirst set of time intervals may include a start time point, a length,and/or an end time point. The first set of time intervals may correspondto a time period in which one of the plurality of first sets of imagingsequences is used to generate a first set of MR image data. In someembodiments, a first time period in which a (or each) first set ofimaging sequences is used may be recorded during scanning, andaccordingly, a second time period in which a corresponding first set ofMR image data are generated based on the first set of imaging sequencesmay be recorded during scanning. In some embodiments, the first timeperiod and the second time period may be the same. For example, a starttime point, a length, and/or an end time point of the first time periodmay be the same as those of the second time period. Thus, a first set oftime intervals in which a first set of MR image data is generated may bedetermined based on the recorded time period(s). Accordingly, theplurality of first sets of time intervals in which the plurality offirst sets of MR image data are generated may be determined.

In 704, the processing device 140 (e.g., the generation module 420, orthe first motion field generation unit 422) may determine at least oneportion of the respiratory motion curve corresponding to the pluralityof first sets of time intervals. As illustrated in FIG. 5, in someembodiments, a generation of at least a portion of the respiratorymotion curve may be simultaneous or synchronous to a generation of theplurality of first sets of MR image data, and thus, a time frame of therespiratory motion curve may be associated with or correlated to that ofthe plurality of first sets of MR image data. That is, the time frame ofthe respiratory motion curve may be associated with or correlated to theplurality of first sets of time intervals. Thus, at least one portion ofthe respiratory motion curve corresponding to the plurality of firstsets of time intervals may be determined. For example, if a first set oftime intervals T_(a) is from time point t₀ to time point t₁, then asegment of the respiratory motion curve corresponding to the timeinterval t₀-t₁ may be retrieved from the respiratory motion curve.Similarly, a plurality of segments of the respiratory motion curvecorresponding to the plurality of first sets of time intervals may bedetermined. The time frame of the respiratory motion curve may include aplurality of timestamps.

In 706, the processing device 140 (e.g., the generation module 420, orthe first motion field generation unit 422) may determine a plurality ofrespiratory phases of the respiratory motion of the object based on theat least one portion of the respiratory motion curve. In someembodiments, as illustrated in FIGS. 5-6, a first set of time intervalsmay include one or more respiratory cycles, and each respiratory cyclemay include one or more respiratory phases. As illustrated in 704, asegment of the respiratory motion curve may correspond to a first set oftime intervals, and thus, the segment of the respiratory motion curvemay correspond to one or more respiratory phases. In some embodiments,the respiratory phases corresponding to a first set of time intervalsmay be determined based on a waveform of the segment of the respiratorymotion curve corresponding to the first set of time intervals. Forexample, if a first set of time intervals includes two respiratorycycles, and each respiratory cycle includes four respiratory phases(e.g., a first respiratory phase, a second respiratory phase, a thirdrespiratory phase, and a fourth respiratory phase), then the firstrespiratory phase of the first respiratory cycle, the second respiratoryphase of the first respiratory cycle, the third respiratory phase of thefirst respiratory cycle, the fourth respiratory phase of the firstrespiratory cycle, the first respiratory phase of the second respiratorycycle, the second respiratory phase of the second respiratory cycle, thethird respiratory phase of the second respiratory cycle, and the fourthrespiratory phase of the second respiratory cycle may be directlyidentified from the waveform of the segment of the respiratory motioncurve corresponding to the first set of time intervals. In someembodiments, a start time point, a length, and/or an end time point ofeach respiratory phase may also be identified from the waveform of thesegment of the respiratory motion curve corresponding to the first setof time intervals.

It should be noted that the above description of process 700 is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, operations 702 and 704 may be integrated into a singleoperation.

FIG. 8 is a flowchart illustrating an exemplary process for generatingtarget motion fields based on a plurality of first sets of motion fieldsaccording to some embodiments of the present disclosure. In someembodiments, at least part of process 800 may be performed by theprocessing device 140 (implemented in, for example, the computing device200 shown in FIG. 2). For example, the process 800 may be stored in astorage device (e.g., the storage device 150, the storage 220, thestorage 390) in the form of instructions (e.g., an application), andinvoked and/or executed by the processing device 140 (e.g., theprocessor 210 illustrated in FIG. 2, the CPU 340 illustrated in FIG. 3,or one or more modules in the processing device 140 illustrated in FIG.4). The operations of the illustrated process presented below areintended to be illustrative. In some embodiments, the process 800 may beaccomplished with one or more additional operations not described,and/or without one or more of the operations discussed. Additionally,the order in which the operations of the process 800 as illustrated inFIG. 8 and described below is not intended to be limiting. In someembodiments, operation 610 illustrated in FIG. 6 may be performedaccording to the process 800.

In 802, the processing device 140 (e.g., the generation module 420, orthe second motion field generation unit 424) may determine a pluralityof second sets of time intervals. In some embodiments, the second setsof time intervals may be determined based on the entire scanning timeperiod (as illustrated in FIGS. 5-6) and a plurality of first sets oftime intervals (e.g., the first sets of time intervals in which theplurality of first sets of MR image data are generated as illustrated inFIGS. 6-7). A second set of time intervals may refer to a time period inwhich no first set of imaging sequences is used. For example, the secondset of time intervals may include a time period no imaging sequence isused (i.e., the object is not scanned). As another example, the secondset of time intervals may include a time period a second set of imagingsequences is used to generate a second set of MR image data. Because theentire scanning time period includes the plurality of first sets of timeintervals in which the plurality of first sets of MR image data aregenerated and a plurality of second sets of time intervals in which theplurality of second sets of MR image data are generated (or no MR imagedata are generated), the plurality of second sets of time intervals maybe determined based on the scanning time period and the plurality offirst sets of time intervals.

In 804, the processing device 140 (e.g., the generation module 420, orthe second motion field generation unit 424) may obtain a plurality ofsecond sets of motion fields based on the plurality of first sets ofmotion fields. In some embodiments, the second sets of motion fields maybe obtained by generating, based on the plurality of first sets ofmotion fields, at least one second set of motion fields corresponding toeach second set of time intervals of the plurality of second sets oftime intervals. In some embodiments, a (or each) second set of motionfields may correspond to a respiratory phase. In some embodiments, oneor more second sets of motion fields (or at least one second set ofmotion fields) may correspond to a second set of time intervals.

In some embodiments, at least a portion of the plurality of second setsof motion fields may be obtained by duplicating the first sets of motionfields. For example, one or more first sets of motion fieldscorresponding to one of the plurality of first sets of time intervalsthat is adjacent to a second set of time intervals may be directlydesignated as the at least one second set of motion fields correspondingto the second set of time intervals. For illustration purposes,referring to FIG. 9, the first set of motion fields (corresponding tothe first sets of time intervals in which the MR image data representedby the region A of the band 903 are generated) may be designated as thesecond set of motion fields (corresponding to the second sets of timeintervals in which the MR image data represented by the region B of theband 903 are generated or no MR image data are generated).

In some embodiments, at least a portion of the plurality of second setsof motion fields may be obtained by fitting the first sets of motionfields. For example, the at least one second set of motion fieldscorresponding to a second set of time intervals may be generated byfitting one or more first sets of motion fields corresponding to two ofthe plurality of first sets of time intervals that are adjacent to thesecond set of time intervals. For illustration purposes, referring toFIG. 9, a motion field may be generated by fitting the first sets ofmotion fields (corresponding to the first sets of time intervals inwhich the MR image data represented by the region A of the band 903 aregenerated) and another first set of motion fields (corresponding to thefirst sets of time intervals in which the MR image data represented bythe region C of the band 903 are generated), and may be designated asthe second set of motion fields (corresponding to the second sets oftime intervals in which the MR image data represented by the region B ofthe band 903 are generated or no MR image data are generated).

In some embodiments, at least a portion of the plurality of second setsof motion fields may be obtained based on a similarity (e.g., asimilarity of the motion curve) between the first sets of time intervalsand the second sets of time intervals. Merely by way of example, thefirst sets of time intervals may include a first plurality ofrespiratory phases (which may be represented by respiratory phases R₁for illustration purposes), and the second sets of time intervals mayinclude a second plurality of respiratory phases (which may berepresented by respiratory phases R₂ for illustration purposes). Therespiratory phases R₁ and the respiratory phases R₂ may refer to samerespiratory phases (e.g., an initial stage of inspiration) of differentrespiratory cycles. Segments of the motion curve corresponding to therespiratory phases R₁ and the respiratory phases R₂ may be determined.Further, a similarity between a segment of motion curve corresponding toeach respiratory phase R₁ and a segment of motion curve corresponding toa specific respiratory phase R₂ may be determined, and thus a pluralityof similarities between the respiratory phases R₁ and the specificrespiratory phase R₂ may be obtained. A second set of motion fieldscorresponding to the specific respiratory phase R₂ may be determinedbased on a weighted sum of the plurality of similarities. Accordingly, aplurality of second sets of motion fields corresponding to a pluralityof second sets of time intervals may be determined. In some embodiments,the similarities may be determined based on the amplitudes, phases,and/or time differences, or other factor(s) relating to segments ofmotion curve between respiratory phases.

In 806, the processing device 140 (e.g., the generation module 420, orthe target motion field generation unit 426) may generate the one ormore target motion fields based on the plurality of first sets of motionfields and the plurality of second sets of motion fields.

In some embodiments, the plurality of first sets of motion fields andthe plurality of second sets of motion fields may be designated as theone or more target motion fields. For illustration purposes, referringto FIG. 9, the plurality of first sets of motion fields (correspondingto the first sets of time intervals in which the MR image datarepresented by the regions A, C and/or E of the band 903 are generated)and the plurality of second sets of motion fields (corresponding to thesecond sets of time intervals in which the MR image data represented bythe regions B, D and/or F of the band 903 are generated or no MR imagedata are generated) may be designated as the one or more target motionfields.

It should be noted that the above description of process 800 is merelyprovided for the purposes of illustration, and not intended to limit thescope of the present disclosure. For persons having ordinary skills inthe art, multiple variations and modifications may be made under theteachings of the present disclosure. However, those variations andmodifications do not depart from the scope of the present disclosure.For example, operations 802 and 804 may be integrated into a singleoperation. As another example, operation 802 may be omitted. In someembodiments, a fitting function may be obtained based on an artificialintelligence model and may be used to fit the plurality of first sets ofmotion fields to obtain the second sets of motion fields or obtain theone or more target motion fields directly.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, device, or device. Program code embodied on a computerreadable signal medium may be transmitted using any appropriate medium,including wireless, wireline, optical fiber cable, RF, or the like, orany suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2103, Perl, COBOL2102, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

What is claimed is:
 1. A method implemented on at least one machine eachof which has at least one processor and at least one storage device forimage processing, the method comprising: obtaining a plurality of firstsets of magnetic resonance (MR) image data of an object generated basedon a plurality of first sets of imaging sequences, the plurality offirst sets of imaging sequences being separated in a scanning timeperiod; obtaining a motion curve of the object, the motion curve beingassociated with a physiological motion of the object in the scanningtime period; obtaining position emission tomography (PET) image data ofthe object generated in the scanning time period; generating one or moretarget motion fields corresponding to the scanning time period based onthe plurality of first sets of MR image data and the motion curve; andgenerating one or more corrected PET images by correcting, based on theone or more target motion fields, the PET image data.
 2. The method ofclaim 1, further comprising: obtaining a plurality of second sets of MRimage data of the object generated based on a plurality of second setsof imaging sequences, the plurality of second sets of imaging sequencesbeing interleaved with the plurality of first sets of imaging sequences.3. The method of claim 2, further comprising: generating one or morecorrected MR images by correcting, based on the one or more targetmotion fields, the plurality of second sets of MR image data.
 4. Themethod of claim 2, wherein the plurality of first sets of imagingsequences are sparsely interspersed between the plurality of second setsof imaging sequences.
 5. The method of claim 1, wherein the motion curveincludes at least one of a respiratory motion curve or a cardiac motioncurve.
 6. The method of claim 1, wherein a generation of at least aportion of the PET image data is simultaneous to a generation of theplurality of first sets of MR image data.
 7. The method of claim 1,wherein the PET image data include PET raw data or data corresponding toone or more PET images reconstructed based on the PET raw data.
 8. Themethod of claim 1, wherein the generating one or more target motionfields corresponding to the scanning time period comprises: obtaining aplurality of first sets of motion fields by generating, based on themotion curve, at least one first set of motion fields corresponding toeach first set of time intervals in which one first set of MR image dataamong the plurality of first sets of MR image data are generated; andgenerating the one or more target motion fields based on the pluralityof first sets of motion fields.
 9. The method of claim 8, wherein themotion curve includes a respiratory motion curve, and the generating,based on the motion curve, at least one first set of motion fieldscorresponding to each first set of time intervals in which one first setof MR image data among the plurality of first sets of MR image data isgenerated comprises: determining a plurality of respiratory phases of arespiratory motion of the object; determining a plurality of pieces ofMR image data corresponding to the plurality of respiratory phases bydetermining, based on the plurality of first sets of MR image data andthe respiratory motion curve, a piece of MR image data corresponding toeach of the plurality of respiratory phases; reconstructing a pluralityof images corresponding to the plurality of respiratory phases based onthe plurality of pieces of MR image data; and generating the at leastone first set of motion fields based on the plurality of imagescorresponding to the plurality of respiratory phases.
 10. The method ofclaim 9, wherein the determining a plurality of respiratory phases of arespiratory motion of the object comprises: determining a plurality offirst sets of time intervals in which the plurality of first sets of MRimage data are generated; determining at least one portion of therespiratory motion curve corresponding to the plurality of first sets oftime intervals; and determining the plurality of respiratory phases ofthe respiratory motion of the object based on the at least one portionof the respiratory motion curve.
 11. The method of claim 9, wherein thedetermining a plurality of pieces of MR image data corresponding to theplurality of respiratory phases comprises: dividing the plurality offirst sets of MR image data into the plurality of pieces of MR imagedata corresponding to the plurality of respiratory phases, based on theplurality of first sets of MR image data and the plurality ofrespiratory phases.
 12. The method of claim 8, wherein the generatingthe one or more target motion fields based on the plurality of firstsets of motion fields comprises: determining, based on the scanning timeperiod and a plurality of first sets of time intervals in which theplurality of first sets of MR image data are generated, a plurality ofsecond sets of time intervals; obtaining a plurality of second sets ofmotion fields by generating, based on the plurality of first sets ofmotion fields, at least one second set of motion fields corresponding toeach second set of time intervals of the plurality of second sets oftime intervals; and generating the one or more target motion fieldsbased on the plurality of first sets of motion fields and the pluralityof second sets of motion fields.
 13. The method of claim 12, wherein thegenerating, based on the plurality of first sets of motion fields, atleast one second set of motion fields corresponding to each second setof time intervals of the plurality of second sets of time intervalscomprises: designating one or more first sets of motion fieldscorresponding to one of the plurality of first sets of time intervalsthat is adjacent to the each second set of time intervals as the atleast one second set of motion fields.
 14. The method of claim 12,wherein the generating, based on the plurality of first sets of motionfields, at least one second set of motion fields corresponding to eachsecond set of time intervals of the plurality of second sets of timeintervals comprises: generating the at least one second set of motionfields corresponding to the each second set of time intervals by fittingone or more first sets of motion fields corresponding to two of theplurality of first sets of time intervals that are adjacent to the eachsecond set of time intervals.
 15. The method of claim 12, wherein thegenerating the one or more target motion fields based on the pluralityof first sets of motion fields and the plurality of second sets ofmotion fields comprises: designating the plurality of first sets ofmotion fields and the plurality of second sets of motion fields as theone or more target motion fields.
 16. The method of claim 8, wherein thegenerating the one or more target motion fields based on the pluralityof first sets of motion fields comprises: generating the one or moretarget motion fields by fitting the plurality of first sets of motionfields.
 17. The method of claim 8, wherein the each first set of timeintervals includes one or more respiratory cycles of the object.
 18. Themethod of claim 1, wherein the plurality of first sets of imagingsequences include at least one of a multi-cycle radial imaging sequence,a spiral imaging sequence, a random imaging sequence, or a radialimaging sequence with a golden-angle scheme.
 19. A system for imageprocessing, comprising: at least one storage device storing a set ofinstructions; and at least one processor in communication with thestorage device, wherein when executing the set of instructions, the atleast one processor is configured to cause the system to performoperations including: obtaining a plurality of first sets of magneticresonance (MR) image data of an object generated based on a plurality offirst sets of imaging sequences, the plurality of first sets of imagingsequences being separated in a scanning time period; obtaining a motioncurve of the object, the motion curve being associated with aphysiological motion of the object in the scanning time period;obtaining position emission tomography (PET) image data of the objectgenerated in the scanning time period; generating one or more targetmotion fields corresponding to the scanning time period based on theplurality of first sets of MR image data and the motion curve; andgenerating one or more corrected PET images by correcting, based on theone or more target motion fields, the PET image data.
 20. Anon-transitory computer readable medium storing instructions, theinstructions, when executed by at least one processor, causing the atleast one processor to implement a method comprising: obtaining aplurality of first sets of magnetic resonance (MR) image data of anobject generated based on a plurality of first sets of imagingsequences, the plurality of first sets of imaging sequences beingseparated in a scanning time period; obtaining a motion curve of theobject, the motion curve being associated with a physiological motion ofthe object in the scanning time period; obtaining position emissiontomography (PET) image data of the object generated in the scanning timeperiod; generating one or more target motion fields corresponding to thescanning time period based on the plurality of first sets of MR imagedata and the motion curve; and generating one or more corrected PETimages by correcting, based on the one or more target motion fields, thePET image data.